**** OPEN OUTPUT LOG FILE  *****


log using "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.APPENDIX F MODELS.F1_F3.04-10-2025.smcl", replace 



**************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************





*** APPENDIX F MODELS: PLACEBO INTEVRENTION MODELS -- 'PLACEBO TREATMENT' IS THE IT REFORM PROGRAM/PROJECT START DATE THROUGH ITS DEVELOPMENT -- ALL 'ADOPTION/TREATMENT' OBSERVATIONS ARE OMITTED FROM THIS EFFECTIVE SAMPLE OF OBSERVATIONS TO ISOLATE ONLY THE PLACEBO INTERVENTION EFFECT [FIGURES F1-F2] ****








********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************






*** MODELS PREDICTING VARIOPUS TYPE OF PROGRAM ERROR RATES BASED ON BAM SAMPLING RATES ***





*** MODEL F1: OVERALL ERROR RATE: (SAMPLE WEIGHTED) ***

*  [# overpayment errors / paid claims sample] + [# underpayment errors / paid claims sample] + [# erroneous denials / denied claims sample] + [# underpayment errors / denied claims sample] *



*** MODEL F2: ABSOLUTE TYPE I ERROR RATE ***

* [overpayment error rate / paid claims sample] *




*** MODEL F3: RELATIVE TYPE I ERROR RATE:  {TYPE I ERROR RATE /  [TYPE I ERROR RATE + TYPE II ERROR RATE]}      (SAMPLE WEIGHTED)  ***

*  {[overpayment error rate / paid claims sample]   /  [overpayment error rate / paid claims sample]   +  [underpayment error rate / paid claims sample]  +  [erroneous denial / denied claims sample]  +  [underpayment error / denied claims sample]}  *






********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************






*** RETRIEVE MANUSCRIPT DATABASE [as of 04-10-2025] ***

use "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\DATA\Performance Management.MANUSCRIPT DATABASE.04-10-2025.dta", replace




***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************




*** SET DATA TO PANEL STRUCTURE  ***

xtset stateid monthyear, monthly

*
*
*
*




***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************



** NOTE: WILL NEED TO EXCLUDE THE FOLLOWING STATE PANELS SINCE THE START DATES OCCUR PRIOR TO THE SAMPLE PERIOD THAT COMMENCES ON 1/2002 **


* NEW MEXICO (1ST IT REFORM ADOPTION/INSTITUTION: NOVEMBER 2002): STATEID==31; PROJECT START DATE: MAY 2001 *

* OHIO (IT REFORM ADOPTION/INSTITUTION: AUGUST 2004): STATEID==35; PROJECT START DATE: JANUARY 2000 *
 
* UTAH (IT REFORM ADOPTION/INSTITUTION: JANUARY 2006): STATEID==44; PROJECT START DATE: OCTOBER 2000 *


drop if stateid==31 | stateid==35 | stateid==44



***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************



*** CREATE 'PLACEBO' INTERVENTION/TREATMENT VARIABLES BASED ON PROJECT START DATE *** 

** create project start date counter that equals "1" for first month of project start date and increase by increments of one per each successive month until the IT reform is adopted/instituted/launched at monthyear "t + s"

gen itmod_monthcount_placebo = itmod_projectstartmonth
* 
*
* replace missing values for itmod_monthcount_placebo based on itmod_projectstartmonth missing values only for the pre-start date 'placebo' intervention/treatment periods [while leave adoption/institution of IT reform periods missing values as appears with itmod_projectstartmonth variable since these are to be excluded from the effective sample for this analysis * 

replace itmod_monthcount_placebo = 0 if itmod_monthcount_placebo==. & itmod_monthcount==0



** create binary 'placebo' treatment indicator [= 0 prior to start date; = 1 during project development phase (start date --> month prior to adoptiojn/launch of IT reforms)] 

gen itmod_monthcount_placebo_binary = 1 if itmod_monthcount_placebo>0
* 
replace itmod_monthcount_placebo_binary = 0 if itmod_monthcount_placebo==0



***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************




*** GENERATE PROJECT START YEAR COHORT UNIT/FE BINARY INDICATORS ***
*** [TO COMPLEMENT STANDARD STATE & YEAR BINARY INDICATORS -- THE SYBIs ACCOUNT FOR SEQUENCE OF STAGGERED PROJECT START DATES ON PERFORMANCE 
*** RESULTING FROM IT MODERNIZATION REFORM 'PLACEBO TREATMENTS' TAKING PLACE] ***



*** PURPOSE: CONTROL FOR STAGGERED/HETEROGENOUS SEQUENCE OF IT MODERNIZATION BEING STARTED AT VARIOUS TIMES IN DIFFERENT YEARS AS PROJECT START YEAR-TIME UNIT EFFECTS ***




*** 2003 IT PROJECT START DATES [MINNESOTA] ***
generate startcohort_2003=1 if stateid==23
*
replace startcohort_2003=0 if startcohort_2003==.



*** 2004 PROJECT START DATES [MISSISSIPPI, NEBRASKA (1ST IT REFORM)] ***
generate startcohort_2004=1 if stateid==24 | stateid==27
* 
replace startcohort_2004=0 if startcohort_2004==.



*** 2005 PROJECT START DATES [ILLINOIS, INDIANA] ***
generate startcohort_2005=1 if stateid==13 | stateid==14
* 
replace startcohort_2005=0 if startcohort_2005==.



*** 2006 PROJECT START DATES [NEW HAMPSHIRE] ***
generate startcohort_2006=1 if stateid==29
* 
replace startcohort_2006=0 if startcohort_2006==.



*** 2007 PROJECT START DATES [MASSACHUSETTS, NEW MEXICO (2ND IT REFORM] ***
generate startcohort_2007=1 if stateid==21 | stateid==52 
* 
replace startcohort_2007=0 if startcohort_2007==.


*** 2008 PROJECT START DATES [FLORIDA] ***
generate startcohort_2008=1 if stateid==9 
* 
replace startcohort_2008=0 if startcohort_2008==.



*** 2009 IT PROJECT START DATES [CALIFORNIA; MISSISSIPPI, NEW HAMPSHIRE, & VIRGINIA] ***
generate startcohort_2009=1 if stateid==5 | stateid==24 |  stateid==29 | stateid==46
* 
replace startcohort_2009=0 if startcohort_2009==.



*** 2010 PROJECT START DATES [ILLINOIS, NEVADA] ***
generate startcohort_2010=1 if stateid==13 | stateid==28
* 
replace startcohort_2010=0 if startcohort_2010==.





*** 2012 PROJECT START DATES [IDAHO, LOUISIANA, MICHIGAN, MISSOURI] ***
generate startcohort_2012=1 if stateid==12 | stateid==18 | stateid==22 | stateid==25
* 
replace startcohort_2012=0 if startcohort_2012==.



*** 2013 PROJECT START DATES [MAINE, NEBRASKA (2ND IT REFORM), NORTH CAROLINA] ***
generate startcohort_2013=1 if stateid==19 | stateid==51 | stateid==33 
* 
replace startcohort_2013=0 if startcohort_2013==.



*** 2014 PROJECT START DATES [TENNESSEE] ***
generate startcohort_2014=1 if stateid==42 
* 
replace startcohort_2014=0 if startcohort_2014==.



*** 2015 PROJECT START DATES [MARYLAND, WASHINGTON] ***
generate startcohort_2015=1 if stateid==20 | stateid==47    
* 
replace startcohort_2015=0 if startcohort_2015==.



*** 2016 PROJECT START DATES [SOUTH CAROLINA] ***
generate startcohort_2016=1 if stateid==40
*
replace startcohort_2016=0 if startcohort_2016==.
*


*** 2017 PROJECT START DATES [ALABAMA, COLORADO, PENNSYLVANIA, WYOMING] ***
generate startcohort_2017=1 if stateid==1 | stateid==6 | stateid==38 | stateid==50    
* 
replace startcohort_2017=0 if startcohort_2017==.





**********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************


*** COMPUTE PROJECT START DATE YEAR COHORT * 'PLACEBO' TREATMENT INTERACTION EFFECTS ***
*** [state group project start date cohort [based on common year of project start date] * 'placebo' treatment effect ***                                                                                                    

*** NOTE: This is activated as "1" starting in the exact month when the IT reform project start date commences within a given year to ensure pre-'placebo' treatment monthly observations wihtin a adoption year take on "0" values] ***

*
*
*

generate startcohort_2003_itstart = startcohort_2003*itmod_monthcount_placebo_binary
*
generate startcohort_2004_itstart = startcohort_2004*itmod_monthcount_placebo_binary
*
generate startcohort_2005_itstart = startcohort_2005*itmod_monthcount_placebo_binary
*
generate startcohort_2006_itstart = startcohort_2006*itmod_monthcount_placebo_binary
*
generate startcohort_2007_itstart = startcohort_2007*itmod_monthcount_placebo_binary
*
generate startcohort_2008_itstart = startcohort_2008*itmod_monthcount_placebo_binary
*
generate startcohort_2009_itstart = startcohort_2009*itmod_monthcount_placebo_binary
*
generate startcohort_2010_itstart = startcohort_2010*itmod_monthcount_placebo_binary
*
*
*
generate startcohort_2012_itstart = startcohort_2012*itmod_monthcount_placebo_binary
*
generate startcohort_2013_itstart = startcohort_2013*itmod_monthcount_placebo_binary
*
generate startcohort_2014_itstart = startcohort_2014*itmod_monthcount_placebo_binary
*
generate startcohort_2015_itstart = startcohort_2015*itmod_monthcount_placebo_binary
*
generate startcohort_2016_itstart = startcohort_2016*itmod_monthcount_placebo_binary
*
generate startcohort_2017_itstart = startcohort_2017*itmod_monthcount_placebo_binary
*
*
*
*     



***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************





*** COMPUTE CATEGORICAL TASK COMPLEXITY COVARIATE MEASURES [CONDITIONAL ADAPTATION TO IT MODERNIZATION REFORMS] ***

** PURPOSE: COMPUTE MARGINAL DIFFERENTIAL EFFECTS IN MANUSCRIPT MODELS [BASED ON EFFECTIVE SAMPLE OF OBSERVATIONS] **



** (1) INTERSTATE CASE RATES [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS F1 & F3];  PAID CLAIMS SAMPLE ONLY: ABSOLUTE TYPE I ERROR RATE [MODEL F2] **


* Overall Program Error Rate *

quietly reg totalerror_rat  itmod_monthcount_placebo  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart  if itmod_adopt_state==1 & itmod_monthcount==0
*
*
sum tot_interstate if e(sample), detail
di r(p75)
di r(p25)
*
gen tot_interstate_catF =.
replace tot_interstate_catF = 0 if tot_interstate<= r(p25) 
replace tot_interstate_catF = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
replace tot_interstate_catF = 2 if tot_interstate>= r(p75) 
*
tab tot_interstate_catF if e(sample)


*
*
*

* Absolute Type I Error Rate *

quietly reg t1error_rat  itmod_monthcount_placebo  t1_interstate    t1_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat  i.stateid i.year  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart  if itmod_adopt_state==1 & itmod_monthcount==0
*
*
sum t1_interstate if e(sample), detail
di r(p75)
di r(p25)
*
gen t1_interstate_catF =.
replace t1_interstate_catF = 0 if t1_interstate<= r(p25) 
replace t1_interstate_catF = 1 if t1_interstate> r(p25) & t1_interstate < r(p75) 
replace t1_interstate_catF = 2 if t1_interstate>= r(p75) 
*
tab t1_interstate_catF if e(sample)
*
*
*

* Relative Type I Error Rate [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *

quietly reg relt1error_rat  itmod_monthcount_placebo  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart   if itmod_adopt_state==1 & itmod_monthcount==0
*
*
sum tot_interstate if e(sample), detail
di r(p75)
di r(p25)
*
gen relt1_interstate_catF =.
replace relt1_interstate_catF = 0 if tot_interstate<= r(p25) 
replace relt1_interstate_catF = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
replace relt1_interstate_catF = 2 if tot_interstate>= r(p75) 
*
tab relt1_interstate_catF if e(sample)
*



*****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************



** (2) DIFFERENT OCCUPATION SEEKING RATE [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS F1 & F3];  PAID CLAIMS SAMPLE ONLY: ABSOLUTE TYPE I ERROR RATE [MODEL F2] **


* Overall Program Error Rate *

quietly reg totalerror_rat  itmod_monthcount_placebo  tot_interstate   tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year   startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart  if itmod_adopt_state==1 & itmod_monthcount==0
*
*
sum tot_diffoccupseek if e(sample), detail
di r(p75)
di r(p25)
*
gen tot_diffoccupseek_catF =.
replace tot_diffoccupseek_catF = 0 if tot_diffoccupseek<= r(p25) 
replace tot_diffoccupseek_catF = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
replace tot_diffoccupseek_catF = 2 if tot_diffoccupseek>= r(p75) 
*
tab tot_diffoccupseek_catF if e(sample)

*
*
*

* Absolute Type I Error Rate *

quietly reg t1error_rat  itmod_monthcount_placebo  t1_interstate    t1_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat  i.stateid i.year  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart   if itmod_adopt_state==1 & itmod_monthcount==0
*
*
sum t1_diffoccupseek if e(sample), detail
di r(p75)
di r(p25)
*
gen t1_diffoccupseek_catF =.
replace t1_diffoccupseek_catF = 0 if t1_diffoccupseek<= r(p25) 
replace t1_diffoccupseek_catF = 1 if t1_diffoccupseek> r(p25) & t1_diffoccupseek < r(p75) 
replace t1_diffoccupseek_catF = 2 if t1_diffoccupseek>= r(p75) 
*
tab t1_diffoccupseek_catF
tab t1_diffoccupseek_catF if itmod_adopt_state==1
*
*
*

* Relative Type I Error Rate [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *

quietly reg relt1error_rat  itmod_monthcount_placebo  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year   startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart  if itmod_adopt_state==1 & itmod_monthcount==0
*
*
sum tot_diffoccupseek if e(sample), detail
di r(p75)
di r(p25)
*
gen relt1_diffoccupseek_catF =.
replace relt1_diffoccupseek_catF = 0 if tot_diffoccupseek<= r(p25) 
replace relt1_diffoccupseek_catF = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
replace relt1_diffoccupseek_catF = 2 if tot_diffoccupseek>= r(p75) 
*
tab relt1_diffoccupseek_catF if e(sample)




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********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************






**** MODELS F1-F3: "TASK COMPLEXITY, ORGANIZATIONAL ADAPTATION & PROGRAM ERROR RATES" 'PLACEBO TREATMENT INTERVENTION' STATISTICAL ANALYSES [JULY 2024]: ORGANIZATIONAL ADAPTATION EFFECTS ON PROGRAM PAYMENT ERROR RATES [FM1: TOTAL PROGRAM ERROR RATE; FM2: ABSOLUTE TYPE I PROGRAM ERROR RATE; FM3: RELATIVE TYPE I PROGRAM ERROR RATE] **** 



** (MODEL F1; FIGURES F1A-1C; MODEL F2: FIGURES F2A-F2C; MODEL F3: FIGURES F2D-F2F) **




************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************




*** TESTING H1 & H3: TOTAL/OVERALL PROGRAM ERROR RATE ORGANIZATIONAL ADAPTATION  ['PLACEBO' INTERVENTION/TREATMENT MODELS] ***



*** ESTIMATE MODEL F1: TOTAL PROGRAM ERROR  RATE [PROPORTION OF SAMPLE-WEIGHTED CASES OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [CONTROLS, PLUS STATE, YEAR, AND YEAR-ADOPTION COHORT UNIT EFFECTS] *** 	(FIGURES F1A-F1C) 

npregress series totalerror_rat  itmod_monthcount_placebo  i.tot_interstate_catF   i.tot_diffoccupseek_catF  if itmod_adopt_state==1 & itmod_monthcount==0, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat    i.stateid i.year  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart)  vce(bootstrap, seed(123) rep(1000))




** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST

predict predsy_m1f if e(sample)
predict residsy_m1f if e(sample), residuals

gen sse_m1f = predsy_m1f * predsy_m1f if e(sample)
gen ssr_m1f = residsy_m1f * residsy_m1f if e(sample)

egen sum_sse_m1f = total(sse_m1f) if e(sample)
egen sum_ssr_m1f = total(ssr_m1f) if e(sample)

gen r2_m1f = sum_ssr_m1f/(sum_sse_m1f + sum_ssr_m1f)

sum r2_m1f


*
*
*

* [MODEL F1: TOTAL ERROR  RATE] FIGURE F1A:  UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]

margins, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))

marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
title(" {bf:FIGURE F1A}""{bf:Unconditional Adaptation Effect}" "{bf:(Total Program Error Rate [MODEL F1])}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
*
*
graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F1.FIGURE F1A.04-10-2025.gph", replace

*
*
*
*

* [MODEL F1: TOTAL PROGRAM ERROR  RATE] FIGURE F1B:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_interstate_cat==2) & LOW COMPLEXITY (tot_interstate_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***

margins r.tot_interstate_catF if tot_interstate_catF==0|tot_interstate_catF==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))

marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
yline(0, lcolor(%40gs) lpattern(shortdash)) ///
legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
title(" {bf:FIGURE F1B}""{bf:Conditional Adaptation Marginal Effect By Task Complexity}" "{bf:(Interstate Claims: Total Program Error Rate [MODEL F1])}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
*
*
*
graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 1.FIGURE F1B.04-10-2025.gph", replace
*
*
*
* [MODEL F1: TOTAL PROGRAM ERROR  RATE] FIGURE F1C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_diffoccupseek_cat==2) & LOW COMPLEXITY (tot_extbenefits_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***

margins r.tot_diffoccupseek_catF if tot_diffoccupseek_catF==0|tot_diffoccupseek_catF==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))

marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
yline(0, lcolor(%40gs) lpattern(shortdash)) ///
legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
title(" {bf:FIGURE F1C}""{bf:Conditional Adaptation Marginal Effect By Task Complexity}" "{bf:(Seeking Different Occupation: Total Program Error Rate [MODEL F1])}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
*
*
*
graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F1.FIGURE F1C.04-10-2025.gph", replace


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*** TESTING H2 & H4: ABSOLUTE TYPE I PROGRAM ERROR RATE ORGANIZATIONAL ADAPTATION ['PLACEBO' INTERVENTION/TREATMENT MODELS] ***



*** ESTIMATE MODEL F2: TYPE I PROGRAM ERROR RATE [PROPORTION OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [ONLY STATE, YEAR, AND YEAR-ADOPTION COHORT UNIT EFFECTS] *** (FIGURES F2A-F2C) 


npregress series t1error_rat  itmod_monthcount_placebo  i.t1_interstate_catF    i.t1_diffoccupseek_catF   if itmod_adopt_state==1 & itmod_monthcount==0, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate  ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat    i.stateid i.year  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart) vce(bootstrap, seed(123)  rep(1000))




** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST

predict predsy_m2f if e(sample)
predict residsy_m2f if e(sample), residuals

gen sse_m2f = predsy_m2f * predsy_m2f if e(sample)
gen ssr_m2f = residsy_m2f * residsy_m2f if e(sample)

egen sum_sse_m2f = total(sse_m2f) if e(sample)
egen sum_ssr_m2f = total(ssr_m2f) if e(sample)

gen r2_m2f = sum_ssr_m2f/(sum_sse_m2f + sum_ssr_m2f)

sum r2_m2f
*
*
*


* [MODEL F2: ABSOLUTE TYPE I PROGRAM ERROR RATE] FIGURE F2A: UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]

margins, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))

marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
title(" {bf:FIGURE F2A}""{bf:Unconditional Adaptation Effect}" "{bf:(Absolute Type I Program Error Rate [MODEL F2])}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
*
*
graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F2.FIGURE F2A.04-10-2025.gph", replace


*
*
*
*

* [MODEL F2: ABSOLUTE TYPE I PROGRAM ERROR RATE] FIGURE F2B:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_interstate_catF==2) & LOW COMPLEXITY (t1_interstate_catF==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***

margins r.t1_interstate_catF if t1_interstate_catF==0|t1_interstate_catF==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))

marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
yline(0, lcolor(%40gs) lpattern(shortdash)) ///
legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
title(" {bf:FIGURE F2B}""{bf:Conditional Adaptation Marginal Effect By Task Complexity}" "{bf:(Interstate Claims: Absolute Type I Program Error Rate [MODEL F2])}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
*
*
*
graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F2.FIGURE F2B.04-10-2025.gph", replace
*
*
*
*
*
* [MODEL F2: ABSOLUTE TYPE I PROGRAM ERROR RATE] FIGURE F2C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_diffoccupseek_catF==2) & LOW COMPLEXITY (t1_diffoccupseek_catF==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***

margins r.t1_diffoccupseek_catF if t1_diffoccupseek_catF==0|t1_diffoccupseek_catF==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))

marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
yline(0, lcolor(%40gs) lpattern(shortdash)) ///
legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
title(" {bf:FIGURE F2C}""{bf:Conditional Adaptation Marginal Effect By Task Complexity}" "{bf:(Seeking Different Occupation: Absolute Type I Program Error Rate [MODEL F2])}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
*
*
*
graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F2.FIGURE F2C.04-10-2025.gph", replace




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*** TESTING H2 & H4: RELATIVE TYPE I PROGRAM ERROR RATE [TYPE I ERROR RATE / (TYPE I ERROR RATE + TYPE II ERROR RATE)] ORGANIZATIONAL ADAPTATION  ['PLACEBO' INTERVENTION/TREATMENT MODELS]***




*** ESTIMATE MODEL F3: RELATIVE TYPE I PROGRAM ERROR RATE [WITH ADDITIONAL COVARIATES: PROPORTION OF SAMPLE-WEIGHTED CASES OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [ONLY STATE, YEAR, AND YEAR-ADOPTION COHORT UNIT EFFECTS] ***  (FIGURES F2D-F2F) 



npregress series relt1error_rat   itmod_monthcount_placebo   i.relt1_interstate_catF   i.relt1_diffoccupseek_catF   if itmod_adopt_state==1 & itmod_monthcount==0, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year   startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart)  vce(bootstrap, seed(123) rep(1000))




** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST

predict predsy_m3f if e(sample)
predict residsy_m3f if e(sample), residuals

gen sse_m3f = predsy_m3f * predsy_m3f if e(sample)
gen ssr_m3f = residsy_m3f * residsy_m3f if e(sample)

egen sum_sse_m3f = total(sse_m3f) if e(sample)
egen sum_ssr_m3f = total(ssr_m3f) if e(sample)

gen r2_m3f = sum_ssr_m3f/(sum_sse_m3f + sum_ssr_m3f)

sum r2_m3f


*
*
*
*
* [MODEL F3: RELATIVE TYPE I PROGRAM ERROR RATE] FIGURE F2D: UNCONDITIONAL ADAPTATION EFFECTS --  E(Y) [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]

margins, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))

marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
title(" {bf:FIGURE F2D}""{bf:Unconditional Adaptation Effect}" "{bf:(Relative Type I Program Error Rate [MODEL F3])}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
*
*
graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F3.FIGURE F2D.04-10-2025.gph", replace

*
*
*
*
* [MODEL F3: RELATIVE TYPE I PROGRAM ERROR RATE] FIGURE F2E: MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_interstate_catF==2) & LOW COMPLEXITY (relt1_interstate_catF==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***

margins r.relt1_interstate_catF if relt1_interstate_catF==0|relt1_interstate_catF==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))

marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
yline(0, lcolor(%40gs) lpattern(shortdash)) ///
legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
title(" {bf:FIGURE F2E}""{bf:Conditional Adaptation Marginal Effect By Task Complexity}" "{bf:(Interstate Claims: Relative Type I Program Error Rate [MODEL F3])}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
*
*
*
graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F3.FIGURE F2E.04-10-2025.gph", replace
*
*
*
*
*

* [MODEL F3: RELATIVE TYPE I PROGRAM ERROR RATE] FIGURE F2F: MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_diffoccupseek_catF==2) & LOW COMPLEXITY (relt1_diffoccupseek_catF==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***

margins r.relt1_diffoccupseek_catF if relt1_diffoccupseek_catF==0|relt1_diffoccupseek_catF==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))

marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
yline(0, lcolor(%40gs) lpattern(shortdash)) ///
legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
title(" {bf:FIGURE F2F}""{bf:Conditional Adaptation Marginal Effect By Task Complexity}" "{bf:(Seeking Different Occupation: Relative Type I Program Error Rate [MODEL F3])}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
*
*
*
graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F3.FIGURE F2F.04-10-2025.gph", replace




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